Hoda Heidari is an assistant professor in machine learning and societal computing at the School of Computer Science, Carnegie Mellon University. Her research is broadly concerned with the social, ethical, and economic implications of artificial intelligence. In particular, her research addresses issues of unfairness and opaqueness through machine learning. Her work in this area has won a best-paper award at the ACM Conference on Fairness, Accountability, and Transparency (FAccT) and an exemplary track award at the ACM Conference on Economics and Computation (EC). She has organized several scholarly events on topics related to Responsible and Trustworthy AI, including a tutorial at the Web Conference (WWW) and several workshops at the Neural and Information Processing Systems (NeurIPS) conference. Heidari completed her doctoral studies in Computer and Information Science at the University of Pennsylvania. She holds an M.Sc. degree in Statistics from the Wharton School of Business. Before joining Carnegie Mellon as a faculty member, she was a postdoctoral scholar at the Machine Learning Institute of ETH Zurich, followed by a year at the Artificial Intelligence, Policy, and Practice (AIPP) initiative at Cornell University.